Complexity AnalysisOOP AND DATA STRUCTURESENGR. JAWAD ALI

http://web.mit.edu/16.070/www/lecture/big_o.pdf Document available on:

Difference between complexity and computation time

Computation time: The interval of solving a problem based on the embedded system architecture. (in the field of computer sciences)

Complexity: The art of handling a problem based on the algorithm designed to solve a case.

OR The difficulty faced by the processor in solving a deployed case on it.

Example

ex = 1 + x + x2/2 + x3/3... x is Real

K=2*3

L=2^3

f(x) = 2 +3x for x = 5

K=2+2+2

L=2*2*2

Big O Notation

Big O notation (with a capital letter O, not a zero), also called Landau's symbol, is a symbolism used in complexity theory, computer science, and mathematics to describe the asymptotic behavior of functions. Basically, it tells you how fast a function grows or declines

Functions defined in big o notation

O(1) constant(slowest) O(log(n)) logarithmic O((log(n))c) polylogarithmic (same as O(log(n)) ) O(n) linear O(n2) quadratic O(nc) polynomial O(cn) exponential(fastest)

Understanding big o

Efficiency covers lots of resources, including: 1. CPU (time) usage (The most important)2. Memory usage 3. Disk usage 4. Network usage

Performance vs complexity

1. Performance: how much time/memory/disk/... is actually used when a program is run. This depends on the machine, compiler, etc. as well as the code.

2. Complexity: how do the resource requirements of a program or algorithm scale, i.e., what happens as the size of the problem being solved gets larger?

More about performance

The time required by a function/procedure is proportional to the number of "basic operations" that it performs, like;

1. one arithmetic operation (e.g., +, *). 2. one assignment (e.g. x := 0) 3. one test (e.g., x = 0) 4. one read (of a primitive type: integer, float, character, Boolean) 5. one write (of a primitive type: integer, float, character, Boolean)

Regarding computing

We express complexity using big-O notation. For a problem of size N: A constant-time algorithm is "order 1": O(1)A linear-time algorithm is "order N": O(N) A quadratic-time algorithm is "order N squared": O(N2) Infinite Time algorithm is “Order infinity”: O(inf)

Finding complexity

Generally, we have 6 cases1. Statements2. If else3. Loop4. Nested loop5. Function call6. When

Statement

statement 1; statement 2; ... statement k;

The total time is found by adding the times for all statements: total time = time(statement 1) + time(statement 2) + ... + time(statement k) If each statement is "simple" (only involves basic operations) then the time for each statement is constant and the total time is also constant: O(1).

If Else

if (cond) then block 1 (statements) else block 2 (statements) end if;

Here, either block 1 will execute, or block 2 will execute. Therefore, the worst-case time is the slower of the two possibilities:

max(time(block 1), time(block 2)) If block 1 takes O(1) and block 2 takes O(N), the if-then-else statement would be O(N)

LOOP

for I in 1 .. N loop sequence of statements end loop

The loop executes N times, so the sequence of statements also executes N times.

If we assume the statements are O(1), the total time for the for loop is N * O(1), which is O(N) overall.

Nested LOOP

for I in 1 .. N loop for J in 1 .. M loop

sequence of statements end loop;

end loop;

The statements in the inner loop execute a total of N * M times. Thus, the complexity is O(N * M).

Function Calls

The behavior of function is same as statement if called once

Its behavior is statement in loop if it is called in loop

Its behavior is more like nested loop if it is called inside loop and it has an characteristic loop inside as well

When

The behavior of such statement is not defined by time or cycles of processing

It may occur the very next moment It might not occur even after the device is expired Such algorithms are limited by some thresholds or bounds, becomes

O(N) Used in training and testing of Artificial Neural Networks and such

End UP…